The color of an object as perceived by a human observer is a complex result of the physical properties of the light illuminating it, the effects on the observer of the surrounding environment and the individual physical and mental characteristics of the observer. Only the first two of these four items can be measured instrumentally with any precision and repeatability. However, the color of most goods marketed in the world economy is often a vital ingredient of their appeal to the buyer and therefore this perceived sensation is of enormous economic importance.
So many companies have, as their principal value added activity, the coloration of goods to a specific perceived target shade, usually by processes involving the applications of precisely calculated mixtures of a few basic colorants. To do this scientifically, they work with instrumentally measured data on the reflectance of the target and the colorants at various wavelengths, combine with data on the illuminants under which a match to a target is required. They may then use mathematical models or algorithms, known as recipe prediction algorithms, which attempt to predict which ratio of basic colorants will give the nearest match of total reflectance to the target reflectance. These recipe prediction algorithms, however, can never perfectly predict the interactions between colorants and substrates and so many researchers have developed many differing recipe prediction algorithms often a particular recipe prediction algorithm is particularly good for one use (e.g., metallic paints) but poor for another (e.g., textiles in UV light).
Many other companies and individuals earn an income making less specific recommendations about color. For example, some paint companies have developed algorithms, known as color recommendation algorithms, which attempt to tell users which paint color will “look good” with a particular carpet color if a “warm” look is needed. Others recommend which dress colors are appropriate for spring weddings, or what colors to wear for a power breakfast, or why black is a bad color for bathrooms.
Because recipe prediction algorithms and color prediction algorithms are optimized for different purposes, it is desirable for users to have access to an assortment of recipe prediction algorithms and color recommendation algorithms, each of which are optimized for a certain purpose. Access to a sufficiently broad assortment is often difficult for the user. Recipe generation algorithms and color recommendation algorithms are usually proprietary, therefore, the user cannot easily build a private collection. Additionally, different algorithms for different purposes are often available with different merchants. It is not necessarily the case that a single company will have an algorithm for a particular purpose. Therefore, the user must locate a company with the particular algorithm best suited for the user's purpose. The various companies with the different algorithms can be sparsely located throughout the world. Therefore, it is often inconvenient for the user to have knowledge and access to each of the companies.
Additionally, the color measurements, which are inputs to these algorithms have to be provided to the algorithm in the form of measurements in accordance with a particular predetermined color measurement standard, such as a reflectance curve or a description of the color using the CIE 1976 L*a*b format. Therefore, even upon location of the particular algorithm, the user must have a way of providing the input colors to the algorithm. Color measurements are provided by measurement of a color sample with an instrument known as a spectrophotometer. The spectrophotometer takes various physical and light wavelength measurements of the color. The result of the foregoing measurements can be provided to the algorithm. However, spectrometers are generally expensive instruments and many users may not have access to them. Such user would need to find other ways to provide the color data to the color algorithm. In other cases, the user may not have an actual sample.
Accordingly, it would be advantageous if the user was provided access to recipe generation algorithms and color recommendation algorithms from vendors worldwide.
It would also be advantageous if users could be provided a simple manner in which to provide the color information for the algorithm inputs.